Ratio of Capital Expenses to Fixed Assets (CF)
5. Empirical Findings and Analysis
Since the Panzar-Rosse model is applicable under the equilibrium conditions only, we first test for the existence of Equilibrium Conditions over the full period as well as for rolling sample of sub-periods extending for five years each. It also helps in identifying the phases or stretches of disequilibrium in the sub-periods of analysis. The table (ROA Rolling) analyses the complete period as well as all the five-year long sub-periods in the given time period. Analysis for the equilibrium test is done based on the rolling data sample with the dependent variable being Return on Assets, to comprehend the shift or transition of the equilibrium conditions along the years. We calculate the value of E or the equilibrium test. It can be mathematically determined by the following formula:
πΈ = π1+ π2 + π3 π€βπππ,
π1 = πΆππππππππππ‘ ππ π ππ‘ππ ππ πΈπππππ¦ππ πΈπ₯ππππ ππ π‘π ππ’ππππ ππ πΈπππππ¦πππ (πΈπΈ) π2 = πΆππππππππππ‘ ππ π ππ‘ππ ππ πΆππππ‘ππ πΈπ₯ππππ ππ π‘π πΉππ₯ππ π΄π π ππ‘π (πΆπΉ)
π3 = πΆπππππππππ‘ ππ π ππ‘ππ ππ π΄πππ’ππ πΌππ‘ππππ π‘ πΈπ₯ππππ ππ π‘π πππ‘ππ πΏπππππππ πΉπ’πππ (πΌπΏ)
The validity of PRH statistic depends upon the assumption of long-run market equilibrium which we have tested in the table (table 5). We check whether the value of E or sum of the values of πΌ1, πΌ2 ππππΌ3is equal to zero or not. We conduct the Wald test for the total period as well as the sub periods putting by testing the following null and alternate hypothesis:
π»0 βΆ πΈ = 0 π»1 βΆ πΈ β 0
The table also shows the values of the estimated coefficients and the value of F-statistic along with its level of significance. The results in the table show that from the period 2000 β 2014, the banking industry is in near equilibrium condition in the long run. The Wald test fails to reject the null hypothesis that E=0.The data for the sub-period shows near zero values of E which points towards the equilibrium conditions. The result for the sub-periods, which includes the recession years, shows empirical evidence of the presence of disequilibrium in the banking industry in the short run. The F-statistic also sustains at a higher level during this period with lower levels of significance which indicates a deviation from the equilibrium condition. This period of disequilibrium corresponds to the period of the global financial crisis.
The results of the dynamic panel, as well as fixed effect models, are presented and compared in Table 7.Alternative estimations are also done to find the robustness of the results in the case of un-scaled revenue and scaled revenue equation.
Table 7 shows the Tests of Equilibrium (Rolling Sample) in Return on Assets
EE= Ratio of Employee Expenses to number of Employees, CF=Ratio of Capital Expenses to Fixed Assets, IL=
Ratio of Annual Interest Expenses to Total Loanable
*,**,*** denote the rejection of null hypothesis at 10%,5%,1% respectively
The banks in the sample are found to be earning their revenues as if under monopolistic competition as in many other emerging market economies.Monopolistic competition is a type of imperfect competition such that many producers sell products that are differentiated from one another as goods but are not perfect substitutes. In monopolistic competition, the firm takes the prices charged by its rivals and ignores the impact of its own prices on the prices of other firms.
Period ln EE (πΆπ) ln CF(πΆπ) ln IL (πΆπ) Sum (E) F-Statistic(Wald test) 2000-2014 0.0635 -0.0176 -0.0745 -0.0286 F(1,785) = 0.1842 2000-2004 -0.0584 -0.0001 -0.1389 -0.1975 F(1,199) = 2.9606*
2001-2005 -0.0053 -0.0075 -0.0057 -0.0186 F(1,206) = 0.0211 2002-2006 -0.1158 0.0573 0.1103 0.0518 F(1,214) = 0.1617 2003-2007 0.0987 -0.0137 -0.1007 -0.0158 F(1,220) = 0.0154 2004-2008 0.3723 -0.0234 -0.1007 0.2482 F(1,225) = 2.5584 2005-2009 1.2682 0.0389 -0.0620 1.2451 F(1,230) = 22.6652***
2006-2010 0.5149 0.0841 -0.0488 0.5502 F(1,234) = 5.3125**
2007-2011 0.3159 0.0822 -0.1898 0.2083 F(1,231) = 1.2618 2008-2012 0.0901 0.0540 -0.2425 -0.0984 F(1,227) = 0.2601 2009-2013 -0.0633 0.0611 -0.2834 -0.2856 F(1,224) = 2.4904 2010-2014 -0.0503 0.0009 0.0029 -0.0465 F(1,221) = 0.1751
Table 8 shows the Fixed Effect and Dynamic Estimation of Total Revenue
EE= Ratio of Employee Expenses to number of Employees, CF=Ratio of Capital Expenses to Fixed Assets, IL= Ratio of Annual Interest Expenses to Total Loanable Funds, Risk=Ratio of Net Provisions for Non-Performing Assets to Total Asset, Size= Natural Logarithm of Total Assets, Capital Ratio=Ratio of Sum of Shareholderβs Capital and Reserves to Total Assets, GDP=GDP Growth Rate
Null 1= There is monopoly (H0: H=0), Null 2= There is perfect competition (H0: H=1)
β*β,β**β,β***β denote significance at 10%,5% and 1% respectively.
βaβ,βbβ,βcβ denote rejection of null hypothesis at 10%,5% and 1% respectively.
Note: J-Statistic-The test for over-identifying restrictions in GMM dynamic model estimation.
AR(1)Arellano-Bond test that average auto-covariance in residuals of order 1 is 0 (H0 implies no autocorrelation).AR(2) Arellano-Bond test that average auto-covariance in residuals of order 2 is 0 (H0 implies no autocorrelation).
(TR) Dynamic Model Fixed Effect Model
2000-2014 2000-2007 2008-2014 2000-2014 2000-2007 2008-2014
Ln (TR(-1)) 0.326*** 0.376*** 0.083*** - - -
The results also support the finding that when the adjustment towards the equilibrium is partial and not instantaneous, the H-statistic is downward biased (Goddard and Wilson,2010).This is clearly evident from the relatively higher values of H-statistic in the case of dynamic estimations as compared to fixed effect estimations. Results show a negative first order autocorrelation in the errors, but this does not imply inconsistency in the results. Inconsistency would be implied if second order autocorrelation is present (Arellano and Bond,1990).
Table 9 shows the Fixed Effect and Dynamic Estimation of Interest Revenue
EE= Ratio of Employee Expenses to number of Employees, CF=Ratio of Capital Expenses to Fixed Assets, IL= Ratio of Annual Interest Expenses to Total Loanable Funds, Risk=Ratio of Net Provisions for Non-Performing Assets to Total Asset, Size= Natural Logarithm of Total Assets, Capital Ratio=Ratio of Sum of Shareholderβs Capital and Reserves to Total Assets, GDP=GDP Growth Rate
Null 1= There is monopoly (H0: H=0), Null 2= There is perfect competition (H0: H=1)
β*β,β**β,β***β denote significance at 10%,5% and 1% respectively.
βaβ,βbβ,βcβ denote rejection of null hypothesis at 10%,5% and 1% respectively.
Note: J-Statistic-The test for over-identifying restrictions in GMM dynamic model estimation.
AR(1)Arellano-Bond test that average auto-covariance in residuals of order 1 is 0 (H0 implies no autocorrelation).AR(2) Arellano-Bond test that average auto-covariance in residuals of order 2 is 0 (H0 implies no autocorrelation).
Dynamic Model Fixed Effect Model
2000-2014 2000-2007 2008-2014 2000-2014 2000-2007 2008-2014
Ln (IR(-1)) 0.256*** 0.192*** 0.126*** - - -
9608.959c 505.010c 6699.872c 663.613c 136.412c 795.597c
Table 10: shows the Fixed Effect and Dynamic Estimations with dependent total revenue scaled by total assets
EE= Ratio of Employee Expenses to number of Employees, CF=Ratio of Capital Expenses to Fixed Assets, IL= Ratio of Annual Interest Expenses to Total Loanable Funds, Risk=Ratio of Net Provisions for Non-Performing Assets to Total Asset, Size= Natural Logarithm of Total Assets, Capital Ratio=Ratio of Sum of Shareholderβs Capital and Reserves to Total Assets, GDP=GDP Growth Rate
Null 1= There is monopoly (H0: H=0), Null 2= There is perfect competition (H0: H=1)
β*β,β**β,β***β denote significance at 10%,5% and 1% respectively.
βaβ,βbβ,βcβ denote rejection of null hypothesis at 10%,5% and 1% respectively.
Note: J-Statistic-The test for over-identifying restrictions in GMM dynamic model estimation.
AR(1)Arellano-Bond test that average auto-covariance in residuals of order 1 is 0 (H0 implies no autocorrelation).AR(2) Arellano-Bond test that average auto-covariance in residuals of order 2 is 0 (H0 implies no autocorrelation).
Dynamic Model Fixed Effect Model
2000-2014 2000-2007 2008-2014 2000-2014 2000-2007 2008-2014
Ln (TR_TA(-1)) 0.340*** 0.180*** 0.042*** - - - 16192.941c 69545.944c 538.476c 140.961c 165.754c 4.561b H0: H=1 F (1,776) = F (1,355) = F (1,296) = F(1,839) = F (1,421) = F(1,360)
23557.235c 3570.425c 22926.825c (632.401) 191.222c 1004.003b
The presence second order autocorrelation is checked to substantiate whether there is inconsistency or not. We find that the second order autocorrelation is insignificant, which implies that there is no inconsistency in the results. Hansen J test shows a case of no over-identifying restrictions and the model seems to be valid in the present context.
Above table 7 exhibits the individual values of the coefficients of each of the independent variables for the complete period, and two sub-periods, which divides the complete term equally into two sub-periods. The value of H statistic for the full sample period is 0.230. The rejection of null hypothesis for H is equal to unity as well as zero, which leads to the rejection of the model for monopoly conjectural variations to short run oligopoly, and perfect competition for both the sub-periods, as well as for the entire period. Results indicate that revenues are earned as if under monopolistic competition as per the Panzar-Rosse Model. The value of the H statistic for the sub-periods 2000-2007 and 2008-2014 points towards a decrease in the degree of competition between the two sub-periods. Specifically in the post-recession period, government control and regulations increased which may have led to a decline in the degree of competition. One of the reasons for the decrease in competition can be the higher requirements or norms for the BASEL II standardized norms.Deregulation in the 1990βs increased the opening up of financial markets.
This also served as an important constituent to increase the competitiveness of banking markets in the first sub-period of the study.The fall in the level of H statistic may be attributed to the consolidation of the sector, with the major banks acquiring smaller banks to gain economies of scale, market share and transaction volume. Competition comes not just from foreign banks but also from the markets. With the growth of derivative transactions and financial markets, corporate and big houses may choose their sources of finance from various banking and non-banking agencies. Even the individuals may park their funds in deposit accounts, and also will be able to choose from money market mutual funds other financial instruments.Nevertheless, even during the post-reform period revenues were earned as if under monopolistic competition.
The coefficient value of lagged dependent variable of total revenue shows that the adjustment towards equilibrium is partial and not instantaneous.The unit price of labour and unit price of capital are all positively significant, however, the results consistently indicate that Interest expenses to total loans which are the unit price of funds significantly are the biggest contributor to the value of H-statistic in both static and dynamic model. This is a strong indicator of the
effects of interest rate liberalisation. The price of capital and price of labour are significant and positive for all the sub-periods as well as the full period sample. With respect to the control variables, capital to asset ratio is significant and positive. This confirms to the BASEL II guidelines wherein banks having higher access to available equity capital may achieve higher growth. So in this case, it implies increased revenue. Size of the banks or the total asset value has a positive and significant impact thus indicating that larger banks fetch higher revenues due to an increased market power. The variable of credit risk is positively significant, which lends support to the view that higher risk may lead to higher revenues. With regard to the macroeconomic factors, the effect of GDP is positive and statistically significant. This highlights the effect of business cycles on the revenue generation.
Considering bankβs core business of interest generation, which have been banks traditional business activity for many years, we use the natural logarithm of interest revenue (table interest revenue) to estimate PRH statistic pertaining to banks' core business as well. In a similar manner, we find estimates of H-statistic downward biased in case of Fixed Effect estimation leading to higher values of H-statistic. We find that in the line of banks, traditional business activity interest revenues are earned as if under monopolistic competition. The value of H-statistic is higher for the first sub-period than the second sub-period as well as the whole sample. This mainly pertains to the fact that the early part of the sample period was marked by an increase in a number of foreign banks due to liberalised entry norms as well setting up of new private sector banks, which then began competing with the public sector banks for market share and earn income specifically pertaining to interesting generation activity. This lead to an increase in competition as measured by the PRH statistic. In the case of interest revenue, the highest contributing factor to H statistic is interest expenses. The unit cost of labour is negatively and statistically significant while the effect of the unit cost of deposits is positive at any given conventional level of significance.
Following the steps of (Misspecifications of PRH), we also estimate the fixed effect and GMM estimations of total revenue scaled by the total asset. Based on the regression analysis, we reject the null hypothesis for both the cases where H-statistic is equal to zero, hence rejecting the fact that the industry has monopoly type of competition, and H-statistic is equal to one, hence rejecting the fact that the industry has perfect competition. Both the unit cost of labour and the unit cost of funds have a significant contribution to H-statistic.
The profit equation points out that the elimination of profits is partial and not instantaneous (the significant value of lagged variable of profit in the profit equation). Although the low persistence of profit values(in the sub-periods), are generally associated with higher competition, but in the case of the Indian banking sector, it may not be implausible to think that a low persistence of profit may arise from other sources, than only competition. Persistence of profits may be a result of incumbent firms enjoying their market power or dominance. As a future scope, we need to map the competitive dynamics of the industry which include the entry threat and market contestability factors.
Figure 4
Source: Based on Authorβs own calculations, based on total assets of the individual banks as compared to the market as a whole
Based on the H-statistic obtained (Table 7, 8), we obtain the information that the revenues are earned under monopolistic competition, and the two sub samples also show a decline in competitive levels. However, the figure above shows a decline in the market concentration of top few banks which would imply an increase in competition according to the classical approach.
Table 1 shows by the concentration index (CR 5 and CR 10) and H statistic values (Table 1).
Conventional views on the relation between competition and market structure such as the structure-conduct-performance paradigm (Bain,1951)) would suggest that more concentrated markets tend to be more collusive (lesser competitive).
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